Neural Network Method Based on Elastic-net Regularization for Solving Inverse Problem
At present,neural network has become one of the hot methods to solve inverse problems.In this paper,the elastic-net regularization is introduced as the penalty term of the loss function in the neural network to prevent the overfitting of the problem,and the algorithm of the neural network based on elastic-net regularization is realized by cross training.Through two numerical experiments of compressive sensing and image deblurring,the feasibility and effectiveness of the elastic-net regularization to prevent overfitting are verified.Furthermore,when the condition number of the transformation matrix is large,better training results can be achieved under lower training rounds.
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